Processing astronomical data is becoming increasingly difficult as datasets grow to terabyte scale. Traditional methods require manual selection of processing tools and significant computational resources, making analysis slow and inefficient. Researchers need a more scalable, automated approach to handle diverse data formats and extract scientific insights faster.
AC3, a Horizon Europe open-source initiative, addresses this challenge by automating the integration and execution of multiple data processing applications. Instead of manually selecting tools, AC3 automatically chooses the best application such as Starlight, PPXF, or Steckmap based on the type of data received. These applications are containerised and deployed across cloud and edge environments, ensuring efficient resource use and reliable performance at scale. With automated provisioning, orchestration, and workload management, AC3 optimises processing time while reducing computational overhead.
AC3 is already being tested with real astronomical datasets from observatories like Roque de los Muchachos in collaboration with the University of Madrid. By simplifying large-scale data processing, AC3 makes it easier for scientists to analyse data more quickly and efficiently.
Join us in Brno to explore how AC3 is transforming astronomy data processing and shaping the future of AI-driven cloud-edge infrastructures.

Traditional farming practices often struggle with inefficiencies, such as over-fertilisation, delayed pest detection, and reactive soil management, leading to reduced yields and environmental impact.

AI-ready data spaces can transform agriculture, as shown by the Horizon Europe Green.Dat.AI research project’s smart farming optimisation pilot, which enables secure data exchange and real-time analytics via AI services. By integrating satellite imagery, drones, and in-situ sensors, the pilot delivers precision fertilisation, early pest detection, and predictive soil health assessments. Key innovations such as federated learning for privacy-preserving edge AI training, digital twins for optimised farm management, and anomaly detection for proactive decisions are harnessed within MyFarm, a widely used Slovenian platform supporting data-driven decision making for farmers and advisors. These advancements refine fertilisation, boost crop predictions, and enhance farm efficiency, paving the way for smarter, more resilient agriculture.

Attendees will gain insights into the technical and practical aspects of implementing AI-driven solutions in agriculture, including challenges, best practices, and future opportunities for innovation.